Column length optimization

It follows that knowing the optimum particle diameter, the optimum column length can also be identified. It must be emphasized that this optimizing procedure... 

Generally, optimizing the selectivity by choosing a gel medium of suitable pore size and pore size distribution is the single most important parameter. Examples of the effect of pore size on the separation of a protein mixture are given in Fig. 2.15. The gain in selectivity may then be traded for speed and/ or sample load. However, if the selectivity is limited, other parameters such as eluent velocity, column length, and sample load need to be optimized to yield the separation required. 

Once the selectivity is optimized, a system optimization can be performed to Improve resolution or to minimize the separation time. Unlike selectivity optimization, system cqptimization is usually highly predictable, since only kinetic parameters are generally considered (see section 1.7). Typical experimental variables include column length, particle size, flow rate, instrument configuration, sample injection size, etc. Hany of these parameters can be. Interrelated mathematically and, therefore, computer simulation and e]q>ert systems have been successful in providing a structured approach to this problem (480,482,491-493). 

Given the construction of the Poppe plot, the number of plates, the column length, the peak capacity, and the particle diameter are determined in the Schoenmakers et al. (2006) scheme all for the first-dimension column. These are then used to determine the second-dimension parameters that include the particle diameter, the number of plates, column length, and peak capacity. Other variables are utilized and optimized from this scheme. 

Several of these points are met by applying the optimization steps discussed above, e.g., using smaller particles, shortening column lengths, and reducing solvent viscosity to reduce backpressure. When we consider a virtual column—a packed bed in a purely theoretical sense— we commonly accept that reducing particle size proportional to column length results in columns with at least the same theoretical efficiency. This is true, but only in the theoretical world. 

Therefore we shall optimize the experimental conditions by looking for the minimum pressure at constant analysis time and efficiency for a given solute pair. It has been shown that this goal is accomplished when the column is operated at the optimum flowrate at which the plate height is minimum (19). The particle size and column length then depend on the plate number and the required analysis time. 

Suppose that you have optimized a gradient on a 0.46 X 25 cm column and you want to transfer it to a 0.21 X 10 cm column. The quotient V2/V is ( nr2L)2/( nr1L), where r is column radius and L is column length. For these columns, V2/Vj = 0.083. Equation 25-8 tells us to decrease the volume flow rate, the sample mass, and the delay time to 0.083 times the values used for the large column. The gradient time should not be changed. 

If the separation is still unsuitable after optimizing the experimental conditions and column length, selectivity must be optimized further by changing the stationary phase, the type of column, or the mobile phase by changing it or adding a modifier. These choices were described earlier in this chapter, because the changes needed depend so heavily on the nature of the analytes and/or sample matrix, it is difficult if not impossible to provide concise, general recommendations. 

Column length is usually optimized around a tradeoff between efficiency and run time. Doubling the column length increases back-pressure and run... 

---